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Does happiness lead to productivity? It might sound intuitive already, but we are obsessed with data. So, we looked into it. And by “we” I mean SlashData and Sentry joined forces to analyse the feedback taken from survey respondents who are professional developers who write software on a regular basis.

To make our filtering even more accurate, it mainly involved experienced developers with at least 10 years of software development experience, as they were required to have a live application. This intentional filtering ensures that the average developer surveyed possesses extensive knowledge and can provide valuable insights into the software development process.

Are happier developers more productive?

Firstly we wanted to identify what makes developers happy and we found:

1. Company size and colleague count don’t significantly impact happiness levels.

2. Whether you’re an experienced coder or new to the field, everyone’s happiness is similar.

3. Delving into infrastructure tasks brings more joy! Devs spending 10 extra hours a week on these issues experience a 3% happiness boost.

4. Managers or those with ‘chief’ titles tend to be 6% happier than their peers.

These insights shed light on what contributes to developer satisfaction in the workplace. Understanding these factors can help foster a more positive and productive environment for all developers.

We conducted an in-depth analysis to uncover valuable developer efficiency insights:

We developed a unique productivity metric by combining three crucial measurements, focusing on how quickly developers complete programming tasks and deploy code to production.

Here’s what our productivity metric considers:

1. Time from code committed to code in production.
2. Time taken to recover from an unexpected outage.
3. Frequency of code deployment to production.

Interestingly, we observed that developers in larger companies tend to take slightly more time to complete tasks compared to their counterparts in smaller organizations. This information provides valuable insights into the dynamics of developer productivity across various company sizes.

What hinders and boosts productivity?

When it comes to barriers, larger companies might experience a slight dip in productivity, with every 500 additional employees contributing to a 1% drop. 

Internal processes and bureaucracy can be culprits, but fear not – we’ll share tips to optimize workflow! Communication is another key player; if it’s smooth sailing, devs thrive, but if not, productivity could plummet by a whopping 48%. However, only 10% of developers face this issue. 

By combining frequency and time metrics, we unveil a cool productivity score measured in hours, allowing us to understand the overall productivity landscape. 

The best part? Happy developers are productive developers! Being 10% happier means completing tasks 10% faster, and each year of experience in software development boosts productivity by 6%. 

Let’s take a closer look at developers’ workloads and what they wish for versus reality! 

The biggest difference lies in dealing with internal messaging, processes, and infrastructure issues. Developers express the desire to allocate 19% and 17% less of their time to these time-consuming tasks. It’s clear that efficient communication and workflow tools are essential for smooth business operations. 

We analyzed their productivity and found that developers spend the most time on software development, followed by project management. 

They spend about 31% and 16% of their week on these tasks.

 Interestingly, developers want to keep doing these tasks as they’re crucial components of their ideal week too. Oh, and here’s a nugget;

the more time they spend coding, the happier they are!

Software development

Let’s dive into how developers spend their time on software development!

Writing code is the most time-consuming activity for 29% of developers, with a whopping 69% spending a lot of their overall time on it. 

The conceptual design phase also takes up significant time, but it’s an enjoyable activity for 60% of developers. However, debugging or fixing code is another time-consuming task, with 67% of devs dedicating a lot of time to it. But here’s the catch – only 51% actually enjoy it. Debugging can be a real workflow challenge and hurt productivity.

What do they feel about their tasks?

Fixing bugs and improving software performance bring joy to 65% of developers. 

They take particular pride in improving software/app performance (21%) and debugging code (12%). 

Writing good code is a big source of pride for 27% of developers, and a total of 69% find pride in this task.

What about the challenges?

The top two challenges are cleaning up legacy code (33% of developers) and running into untested code (32%).

Interestingly, cleaning up legacy code was more common in larger teams, where devs work with a 12% bigger team. But don’t worry, larger teams have more resources for testing, so running into untested code isn’t as big of a challenge for them.

Now, onto the root causes of issues. A whopping 37% of devs say a rushed timeline is the biggest problem they face. Among programmers and software developers, 45% identify rushed timelines as a key challenge, 14 percentage points more than CEOs and managers (31%).

Let’s explore the challenges faced by developers in different roles.

We’ll break it down by the prominent positions, such as management/chiefs, programmers/software developers, architects, and IT workers.

Surprisingly, shifting and unclear priorities are among the top three obstacles across all roles, but they’re especially prominent for programmers/software developers and managers/chiefs. Another common challenge for everyone, but particularly for architects, is too many meetings.

Interestingly, many of the top challenges reported in all roles are process-related. This emphasizes the importance for companies to implement good policies and procedures to optimize workflow and boost developer productivity.

Since the beginning of computing, women have consistently played a pivotal role in software development that has frequently been overlooked; from Ada Lovelace developing the first algorithms for modern computers to Margaret Hamilton’s crucial role in the development of on-board guidance software for NASA’s Apollo program.

However, despite women’s exceptional contributions to the field, they have often received less credit than their male counterparts, and their place in the field is questioned.

Today, efforts are being made across the software development ecosystem to address these historical biases. While efforts have been made to promote women to get involved in the historically male-dominated field, there is still considerable work to be done. 

Data and technology are not free from bias. Past applications and software development projects have demonstrated the need for input from diverse groups2.

In this chapter, we specifically explore the involvement of women in software development. According to our latest global developer survey (Q1 2023), nearly a quarter of all developers (22%) self-identify as females, the highest proportion since we began asking respondents about their gender.

This is a small increase from two years ago, since Q1 2021, when female coders accounted for 19% of all developers. 

This slight increase in the proportion of developers self-identifying as females can be partially attributed to the rise in the representation of women among early-to-mid-career developers. Women currently make up a quarter (25%) of developers between the ages of 25 and 34, the highest proportion of all age groups, up from less than 20% in Q1 2021.

This is followed closely by 23% of developers between the ages of 18 and 24. The highest proportion of women falling within the 25-34 age bracket indicates the possible beginning of a positive trend for the future of women in the tech industry. This is the age when people begin to settle into their careers and is a point where people are likely to develop additional skills that allow them to cross-train and enter industries of their choosing.

Further to this, we are also seeing an increasing presence of women in certain regions that are leading to an increase in the proportion of women in technology overall.

Specific highlights include the Middle East and Africa, where the proportion of women in technology in this region has gone from 10% in Q1 2021 to over 20% currently. Similarly, women made up 15% of developers in East Asia in Q1 2021 and now makeup almost 30% of developers. 

Overall, a higher representation of women in the software development ecosystem is a great development. Not only do they bring critical perspectives and approaches to the work being undertaken, but diversity in the workforce offers fresh experiences that can help businesses address underserved needs.

It also enhances efforts to make spaces that are less hostile to women in both overt and subtle ways, allowing even more women to follow their interests in the technology space.

The proportion of women among developers varies substantially depending on the types of projects they are involved in. Virtual reality (VR) and augmented reality (AR) projects have the highest proportion of women, at 33% and 28% respectively, followed by games (28%). 

On the contrary, backend services and web application projects have the lowest concentration of female coders, at just 13% and 16%, respectively.

With these sectors selecting those with formal degrees at higher rates than other areas, and a 10 percentage point difference between men and women having such a degree, this may be one factor in the lower presence of women.

Undergraduate degrees in computer science or equivalent are held by 45% of backend developers and 43% of web developers, compared to 37% of all developers.

Further, the lower proportion of women working in backend services and web application development may, in part, be attributed to the historically male-dominated culture within these sectors. Addressing cultural differences3 and fostering a more inclusive atmosphere can contribute to balancing representation and mentorship opportunities within these sectors.

Further, there may be potential unconscious biases in hiring practices derived from existing workplace culture, which may prevent certain development areas from harnessing the full spectrum of talent, and benefit from the input of individuals with diverse backgrounds.

Examining the sizes of organisations that female developers work for throughout various stages of their life and career could indicate that company characteristics have an influence on women’s decisions in the technology sector.

Like young men, young women are more likely to work as freelancers relative to other age groups and only return to similar proportions among developers aged 55 and above. Additionally, younger female developers (18-24) tend to work for smaller companies, whereas older female developers (45+) are more inclined to work for larger organisations with over 10,000 employees.

Examining a particular age group, women between the age of 35 and 44, may offer an insight into issues women have with progressing through their careers. Previous research into women’s careers in the software development sector has highlighted that women are promoted at a lower rate than men4.

However, when looking at the roles women self-identify with, we find that at mid-market companies (251-1,000 employees) and enterprises (1,001-10,000 employees) the percentage of women in management positions (20% and 29%) is significantly higher than at other organisation sizes (13% on average).

These organisations could offer better opportunities for career growth, decision-making, and leadership. In larger companies, management roles might be more hierarchical and bureaucratic, leading to less autonomy and slower career progression.

In smaller companies, limited opportunities due to their size might result in fewer leadership positions being available overall, and with women being a minority in software development, there are fewer women in leadership positions.

Further, there is an underrepresentation of women in certain leadership roles. 11% of men list their role as CIO, CTO, or IT manager, and 14% identify as technical team leads, compared to just 9% and 8% of women. This could create a cycle whereby there may be fewer mentorship opportunities for other women.

When there are fewer female leaders, it has been found in a range of fields5 that it can be harder for women to progress in their careers, and it can be more challenging for aspiring women to find mentors who can guide them, provide valuable insights, and help them navigate their career paths. 

However, while still a minority of those in such roles, 25% of those in CEO or management positions are women, compared to their position as 22% of the developer population.

While only a small percentage difference, given their underrepresentation in other leadership roles, this represents an area where women are getting leadership positions. Among the previously discussed issues women may face, women are also less likely to apply for leadership positions where they do not fulfil all of the requirements than men6.

This may be leading women to also self-select towards management positions that are not solely dependent on technical skills. 

The observation that women hold a higher proportion of CEO/management roles compared to men (7% against 5%, respectively), particularly in companies with more than 250 employees (8% of women to 4% of men), could indicate a positive shift in gender representation and diversity in leadership positions.

This trend might be driven by a changing corporate culture that is increasingly recognising the importance of gender diversity in leadership, leading companies to seek out and promote women into these roles6 proactively.

Embracing diverse perspectives at the decision-making level can result in better organisational performance and decision-making.

Another factor that may contribute to this observation is the growing appreciation for women’s leadership styles, which tend to be more collaborative, participative, and relationship-oriented. These qualities are often valued in today’s business environment and might make women particularly well-suited for CEO/management roles.

Moreover, women, through their skills and abilities, are likely actively contributing to this positive trend, demonstrating that they are well-equipped for leadership roles. Despite women remaining a minority in leadership this growing representation in CEO/management roles is a step in the right direction, highlighting the benefits of diverse and inclusive leadership.

Software development projects are like puzzles with countless pieces that must come together seamlessly. But one of the biggest challenges in this process is estimating and planning the project accurately. It’s a bit like trying to predict the future-a daunting task. However, with the introduction of AI-powered predictive analytics and the emergence of AI-based project management tools, a new era of software development project estimation and planning has begun.

Understand the terms

Project estimation and planning

Project estimation and planning in software development involves predicting the project’s duration, effort, and resource requirements. Project managers and teams break down the project into smaller tasks, estimate the time and effort for each task, and create a timeline. They consider factors like team size, skills, and available resources. The goal is to set realistic expectations and use resources wisely. Good estimation and planning prevent surprises, delays, and extra costs. It’s about understanding what needs to be done, how long it will take, and what resources are necessary for success.

Most of the time, the estimation process would cost the company significant money and time at the start of developing a brand-new website, app, or software.

AI-powered predictive analytics

AI algorithms can predict future events or behaviours by analyzing large amounts of data and identifying patterns. AI-powered predictive analytics in software development can estimate project timelines, identify risks, and optimize resource allocation. It enables us to make data-driven decisions and adjust plans as necessary. It’s all about using AI to predict and plan for the future based on insights from past data.

Project Estimation and Planning Before AI

Before the introduction of AI-powered predictive analytics, project estimation and planning in software development relied heavily on human expertise and historical data. Project managers and teams would analyze previous projects with similar characteristics and use their experience to estimate the effort, time, and resources required for the new project. The following are some notable challenges of traditional project estimation and planning.

Limited data insights

The amount of historical data available for analysis limited traditional methods. Estimates were frequently based on a few previous projects, which may not accurately represent the complexities of new projects.

Biases and assumptions

Estimates may be influenced by human biases and assumptions, resulting in overestimation or underestimation of effort and timelines. These biases may result from previous experiences or personal perspectives, affecting the accuracy of estimations.

Identifying risks

Another challenge was anticipating potential risks and challenges early in the planning process. Due to the lack of comprehensive data analysis capabilities, project managers relied on their intuition and experience, which may have covered only some potential risks.

Adaptability and optimization

Traditional methods lacked the flexibility to adjust estimates and plans as the project progressed. Real-time data integration was limited, preventing optimal decision-making and resource allocation based on changing project needs.

Enter: AI-Powered Predictive Analytics

The implementation of AI-powered predictive analytics has changed the process radically. By leveraging machine learning and data analysis, AI can analyze vast amounts of historical project data to identify patterns, trends, and correlations that humans might miss. Here are some of the ways AI transforms project estimation and planning:

Uncovering hidden insights

AI algorithms examine massive amounts of historical project data, detecting patterns, trends, and correlations humans may overlook. AI uncovers hidden insights that enable more accurate predictions by analyzing project variables such as scope, complexity, team size, and resource allocation.

Data-driven decision-making

Project managers and stakeholders can make data-driven decisions from the start with AI-powered predictive analytics. They gain insight into potential bottlenecks, allowing them to allocate resources better. AI provides realistic timelines, enabling stakeholders to set appropriate expectations and avoid overpromising or underdelivering.

Effective risk management

AI identifies potential risks early on by analyzing historical project data. It identifies factors that have historically resulted in delays or cost overruns. With this information, project managers can proactively mitigate risks and develop contingency plans, resulting in more efficient project execution.

Continuous improvement

AI algorithms learn from real-time project data, adapting and refining their predictions. AI provides valuable insights as projects progress, allowing teams to course-correct, make data-driven decisions, and optimize resource allocation. Over time, this iterative learning process improves estimation accuracy.

Human-AI collaboration

It is critical to understand that AI-powered predictive analytics does not replace human expertise but supplements it. Project managers and stakeholders contribute valuable experience and domain knowledge. AI provides them with new insights, enhancing their decision-making abilities.

Final thoughts

AI-powered predictive analytics has transformed software development project estimation and planning. It enables project managers and teams to make more accurate predictions, optimize resource allocation, and manage risks more proactively. We can uncover hidden insights, make data-driven decisions, and adapt plans in real time by leveraging AI’s data analysis capabilities. 

This new era of project estimation and planning combines the best of human expertise with the power of artificial intelligence, resulting in more successful and efficient software development projects. We can expect even greater accuracy and efficiency in the future as AI technology advances, paving the way for continued innovation and growth in the software development industry.

You’re familiar with at least one AI-assisted development tool; That’s right, the ChatGPT. Its popularity has skyrocketed in the last few months and with good reason.

It is designed to assist users in generating human-like text but it’s been helpful to developers too, as they can leverage ChatGPT to automate certain tasks, generate code snippets, assist in writing documentation, or even prototype conversational interfaces. While ChatGPT is primarily a language model, it can be used in the development process to aid in various aspects of software development.

In SlashData’s  24th edition of State of Developer Nation, we asked developers if they use AI and how. This led to a dedicated chapter on all the new technologies that captivate developers’ imaginations.

The data from the survey suggest that 63% of developers engaged in some aspect of AI-assisted development, making it evident that this technology is rapidly maturing and transforming from a mere trend to a valuable tool.

AI-Assisted Development: A Growing Trend

While overall engagement has experienced a slight decline of 4% over the past year, the nature of developer involvement has undergone a fascinating shift.

More developers are actively working on or learning about AI-assisted development, showing a 6% increase in engagement. 

Simultaneously, the number of developers with latent interest has decreased by 6%.

This dynamic suggests that AI-assisted development is maturing and gaining practical applicability in the development landscape.

Generative AI: Unleashing Creative Possibilities

Alongside AI-assisted development, generative AI has emerged as a new and exciting technology. 

With 57% of developers actively involved or interested in generative AI, curiosity and excitement abound. While AI-assisted development still leads in adoption at 17%, generative AI projects attract 14% of engaged developers.

The Many Uses of Generative AI

Developers use generative AI in three main ways: 

• as a helpful tool for their development process
• by integrating it into projects through APIs
• or even by creating the models themselves.

Ongoing investigations are exploring these usage patterns to uncover more insights into this groundbreaking technology.

Challenges and Opportunities

Although generative AI is gaining high engagement, there are factors that affect its adoption among developers.

Some developers may be hesitant to rely solely on generative models for critical or security-conscious tasks. 

However, there is a growing adoption of generative AI for visual assets in software development, which reduces the risks of errors and security vulnerabilities.

Overcoming Challenges

Developers who work on generative AI models face the challenge of needing a large amount of training data. 

However, certain tools offer the ability to fine-tune pre-trained models for specific tasks, making this challenge easier to overcome. As developers become more familiar with assistive and generative AI technologies, we can expect a surge in their adoption, leading to innovation and creativity.

Leadership’s Role

Interestingly, leaders in C-suite and other leadership positions show higher engagement rates with emerging technologies. 

About 49% and 50% of those who approve tool expenses or budgets are actively involved in AI-assisted development. 

This trend suggests that the revolution in AI-assisted development is driven by leaders who recognize its potential.

Looking Ahead: The Changing Landscape:

When we take a broader view, we see a cyclical pattern in the adoption and interest in emerging technologies. Developer interest has dropped by 5% overall, while adoption has increased by 4 percentage points. 

This contrast indicates a dynamic shift in developer preferences, marking a change from previous trends.

In summary, AI-assisted development is rapidly evolving and attracting developers’ attention. Generative AI opens up exciting possibilities, and leadership engagement plays a crucial role in driving its growth. Cryptocurrencies continue to be intriguing, and the landscape of emerging technologies is constantly shifting. 

Did you find this article interesting? Download the free report to learn about: 

• The rest of the technologies that capture the developers’ imagination
• The Role of female coders in software development
• An update on language communities
• How well-paid developers feel
• What makes a high-quality API
• An Overview of embedded software development